Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic
knowledge and provides expedited solutions to complex challenges. Remarkable …

Artificial intelligence (AI) has transformed pharmacological research through machine
learning, deep learning, and natural language processing. These advancements have …

The retrieval of hit/lead compounds with novel scaffolds during early drug development is an
important but challenging task. Various generative models have been proposed to create …

Generative molecular design for drug discovery and development has seen a recent
resurgence promising to improve the efficiency of the design-make-test-analyse cycle; by …

Several studies applying Machine Learning to deception detection have been published in
the last decade. A rich and complex set of settings, approaches, theories, and results is now …

A modern-day physician is faced with a vast abundance of clinical and scientific data, by far
surpassing the capabilities of the human mind. Until the last decade, advances in data …

Cancer progression is linked to gene-environment interactions that alter cellular
homeostasis. The use of biomarkers as early indicators of disease manifestation and …

A persistent goal for de novo drug design is to generate novel chemical compounds with
desirable properties in a labor-, time-, and cost-efficient manner. Deep generative models …

The efficient exploration of chemical space to design molecules with intended properties
enables the accelerated discovery of drugs, materials, and catalysts, and is one of the most …

The advent of artificial intelligence (AI) has catalyzed a profound transformation in the
pharmaceutical industry, ushering in a paradigm shift across various domains, including …